Connect all key internal documents so they can be queried for policy, process, or training purposes.
Ingest earnings calls to summarize and provide insight for investment strategies, company performance, and potential risks.
Connect to leading news sources to have curated summaries by department, and reduce time searching for relevant information.
Upload internal or external documentation and inspect/interrogate the information to uncover key details.
As the landscape of alternative asset management continues to evolve, the intersection of technology and data stands as a cornerstone for innovation and maintaining a competitive edge. Many hedge funds are looking to external and internal data sources to capture trends, uncover information, and provide insight around operational opportunities. Large language models (LLMs) serve a growing use case around this data, as they provide an amazing ability to summarize unstructured data.
While existing models like BloombergGPT have demonstrated remarkable capabilities, they often fall short in addressing the nuances of organizational-specific use cases for LLMs. Fine-tuning improves model performance, but has its limits due to the specific training the fine-tuning is specialized for. Enter Retrieval Augmented Generation (RAG).
RAG fundamentally improves use case performance by enabling LLMs to access organizational context in real time. Whether it’s industry news, market data, recorded earnings calls, or internal emails, these data sources can provide needed context for the LLM. The Shakudo platform is a catalyst in the realization of RAG-based LLM architecture for hedge funds.
At Shakudo, our mission is to be more than a platform. We aim to be a strategic partner, empowering technology and data leaders in hedge funds to navigate the complexities of AI and data with confidence and agility.
Get in touch with the Shakudo team to learn more about how our platform can enable your fund to stand up a RAG-based LLM!